计算机应用 ›› 2019, Vol. 39 ›› Issue (4): 1229-1233.DOI: 10.11772/j.issn.1001-9081.2018102087

• 应用前沿、交叉与综合 • 上一篇    下一篇

风电机组齿轮箱的多变量时间序列故障预警

刘帅1, 刘长良1,2, 甄成刚1   

  1. 1. 华北电力大学 控制与计算机工程学院, 北京 102206;
    2. 新能源电力系统国家重点实验室(华北电力大学), 北京 102206
  • 收稿日期:2018-10-15 修回日期:2018-12-09 发布日期:2019-04-10 出版日期:2019-04-10
  • 通讯作者: 刘长良
  • 作者简介:刘帅(1990-),男,河北安国人,博士研究生,主要研究方向:风电机组故障预警;刘长良(1965-),男,河北沧州人,教授,博士,主要研究方向:系统建模与仿真、故障诊断;甄成刚(1964-),男,河北藁城人,教授,博士,主要研究方向:故障预警、故障诊断。
  • 基金资助:
    北京市自然科学基金资助项目(4182061);中央高校基本科研业务费专项资金资助项目(2018ZD05,9163116001)。

Multivariate time series fault warning for wind turbine gearbox

LIU Shuai1, LIU Changliang1,2, ZHEN Chenggang1   

  1. 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
    2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University), Beijing 102206, China
  • Received:2018-10-15 Revised:2018-12-09 Online:2019-04-10 Published:2019-04-10
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Beijing (4182061), the Fundamental Research Funds for the Central Universities (2018ZD05, 9163116001).

摘要: 针对风电机组故障预警中,原始动态时间规整(DTW)算法无法有效度量风电机组多变量时间序列数据之间距离的问题,提出一种基于犹豫模糊集的动态时间规整(HFS-DTW)算法。该算法是原始DTW算法的一种扩展算法,可对单变量和多变量时间序列数据进行距离度量,且精度与速度较原始DTW算法更优。以子时间序列相似度距离为目标函数,使用帝国竞争算法(ICA)优化了HFS-DTW算法中的子序列长度和步距参数。算例研究表明与仅DTW算法和非参数最优的HFS-DTW算法相对比,参数最优的HFS-DTW可挖掘更多的多维特征点信息,输出的多维特征点相似序列具有更丰富细节;且基于所提算法可提前10天预警风电机组齿轮箱故障。

关键词: 风电机组, 故障预警, 犹豫模糊集, 帝国竞争算法, 动态时间规整

Abstract: For wind turbine fault warning, original Dynamic Time Warping (DTW) algorithm cannot measure the distance effectively between two multivariate time series data of wind turbines. Aiming at this problem, a DTW algorithm based on Hesitation Fuzzy Set (HFS-DTW) was proposed. The algorithm is an extended algorithm of the original DTW algorithm, which can measure the distance of both univariate and multivariate time series data, and has higher accuracy and speed compared to the original DTW algorithm. With the sub-sequence similarity distance applied as cost function, the length of sub-sequence and step parameters in HFS-DTW algorithm were optimized by using Imperialist Competitive Algorithm (ICA). The study shows that compared to the only DTW algorithm and the HFS-DTW algorithm with non-optimal parameter, the HFS-DTW with optimal parameter can mine more information on multi-dimensional feature point, and the output multi-dimensional feature point similar sequence has more details. And based on the proposed algorithm, the wind turbine gearbox fault can be warned 10 days in advance.

Key words: wind turbine, fault warning, Hesitant Fuzzy Set (HFS), imperialist competitive algorithm, Dynamic Time Warping (DTW)

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